Traffic simulation is a useful tool for analyzing traffic flows when designing roads, highways, tunnels, bridges, and other vehicular traffic ways. It can help to answer many “what-if” questions prior to field construction; compare and determine the trade-offs between scenarios such as different network configurations, suitable placements for signs, optimal timing of traffic signals, and the like. By analyzing the flow of vehicles over a road network, a municipality can improve the road network and traffic management to make more effective use of the existing infrastructure and/or accurately project future travel demand and supply shortage, thus plan necessary expansion and improvement of the infrastructure to accommodate growth in traffic.
However, the ability to model traffic flow requires the appropriate analytical systems and techniques for analyzing complex and dynamic systems. Because of the many complex aspects of a traffic system, including driver behavioral considerations, vehicular flow interactions within the network, stochasticity caused by weather effects, traffic accidents, seasonal variation, etc., it has been notoriously difficult to estimate traffic flows over a road network.
There exist traffic simulators for modeling the traffic flow across road networks. Vehicle counts, speeds, and other traffic data over time and various locations are being collected to calibrate and validate the traffic models. The planers and engineers can experiment with these models to analyze how traffic may flow as volume increases, accidents reduce available lanes and other conditions vary.
Although these traffic simulation tools are helpful, they are not easy to use and require a labor intensive process for the preparation of data input and interpretation and analysis of simulation output. Often a user has to spend days preparing the input data to apply to a simulator of a road network. Moreover, the size of the road networks existing simulators can handle, or the level of details these simulators can provide are often limited.
A further drawback to these existing systems is that these models lack accurate geographical representation of network objects. Specifically, many existing systems employ the traditional “links and nodes” graph formulation of traffic network, with each node representing an intersection or a change of traffic characteristics along the road, and each link representing the roadway connecting the two end nodes. The position of nodes and/or links are represented by their 2D Cartesian coordinates of X and Y, and do not necessarily align to their true geographical locations. As a result of the arbitrarily chosen coordination systems, it is often difficult to accurately geocode the survey data, and reference data from different sources. Furthermore, the lack of geographically accurate road network data also results in inaccurate model output because of the errors in measurement of distance and length.
Traffic simulation tools in general are computational demanding because of the complexity involved in modeling traveler behavior and because numerous network objects and vehicles need to be tracked. This is particularly true for the microscopic traffic simulator in which vehicle movements are modeled in detail on a second-by-second basis. On the other hand, some more aggregate models have been developed to simulate large networks, but they do not provide the necessary details in representing the traffic dynamics in modeling traffic signal operations. As a result, neither models may be sufficient for detailed traffic engineering applications of a large scale urban network. However, these congested urban networks are exactly the areas whose severe traffic problems need to be better studied and relieved.
Accordingly, today planners and engineers face significant disadvantages, as current traffic simulation tools do not generally scale to large urban areas in a manner that conserves calculation resources while providing meaningful simulation results. As a further disadvantage, current tools do little to make design and testing easier for users.